Deep Learning Reconstruction Enables Highly Accelerated Biparametric <scp>MR</scp> Imaging of the Prostate

Johnson, Patricia M.; Tong, Angela; Donthireddy, Awani; Melamud, Kira; Petrocelli, Robert; Smereka, Paul; Qian, Kun; Keerthivasan, Mahesh Bharath; Chandarana, Hersh; Knoll, Florian

doi:10.1002/jmri.28024

Cited by 42 publications

(32 citation statements)

References 37 publications

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“…Initially, in prostate MRI, machine learning approaches have been applied mostly for lesion detection and classification [ 28 , 29 , 30 ]. However, recent studies also focused on scan acceleration in prostate MRI [ 18 , 31 , 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies demonstrated the potential value of DL-based approaches for retrospectively accelerated MRI of the prostate MRI and the knee. [ 17 , 18 ] However, to study the clinical performance of any acceleration method, it is essential to investigate the effects of prospective undersampling on image quality. Interactions between the actual k-space sampling scheme and physiological motion or eddy currents related to pseudo-random sampling might impact the quality of the acquired k-space data.…”

Section: Discussionmentioning

confidence: 99%

“…Promising results were also reported for the MRI of the prostate. However, they warrant further evaluation, as previous studies relied on retrospectively undersampled data from fully sampled acquisitions [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer

Harder

Weiss

Amiel

et al. 2022

Cancers

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Background: To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa). Methods: Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles). Results: All C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ. Conclusion: In this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer

Harder

Weiss

Amiel

et al. 2022

Cancers

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“…36 In the context of deep-learning, the soft-SENSE model has been used recently. [37][38][39] VarNet and MoDL only update the image corresponding to the first set of coil sensitivity maps in the network block. We use the MSE loss on the coil images since the coil images serve as a reference independent of the estimated coil sensitivity maps.…”

Section: Extensions To the Sense-modelmentioning

confidence: 99%

Deep, deep learning with BART

Blumenthal

Luo

Schilling

et al. 2022

Magnetic Resonance in Med

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Purpose To develop a deep‐learning‐based image reconstruction framework for reproducible research in MRI. Methods The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a nonlinear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI‐specific operators of BART, such as the nonuniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep‐learning‐based reconstruction, two state‐of‐the‐art unrolled reconstruction networks, namely the Variational Network and MoDL, were implemented. Results State‐of‐the‐art deep image‐reconstruction networks can be constructed and trained using BART's gradient‐based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. Conclusion By integrating nonlinear operators and neural networks into BART, we provide a general framework for deep‐learning‐based reconstruction in MRI.

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“…The soft-SENSE model is suitable if the object exceeds the FOV since one set of coil sensitivity maps can not explain infolding artifacts [33]. In the context of deeplearning, the soft-SENSE model has been used recently in [34,35]. BART's implementations of VarNet and MoDL only uses the image corresponding to the first set of coil sensitivity maps in the network part of the soft-SENSE model.…”

Section: Extensions To the Sense-modelmentioning

confidence: 99%

Deep, Deep Learning with BART

Blumenthal¹,

Luo²,

Schilling³

et al. 2022

Preprint

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Purpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a non-linear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the non-uniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network [1] and MoDL [2], were implemented. Results: State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. Conclusion: By integrating non-linear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI.

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Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate

Cited by 42 publications

References 37 publications

Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer

Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer

Deep, deep learning with BART

Deep, Deep Learning with BART

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